The present invention relates to an on-line system identification method used in adaptive echo canceller, adaptive noise canceller or the like, and more particularly to high speed processing and sequential updating processing of blind system.
The meanings of symbols used in the invention are shown in Table 1.
A conventional system identification method is described while referring to FIG. 8. FIG. 8 is a block diagram showing a conventional system identification device. In FIG. 8, from one input line 501, a digital signal x (n) is transmitted through two unknown systems 502, 504, and digital signals y1 (n) and y2 (n) are output to output lines 506, 509. A first system identification device 503 is connected parallel to the first unknown system 502, and a second system identification device 505, to the second unknown system 504. To these system identification devices, a digital signal x (n) is entered same as in the unknown systems, and corresponding signals (formula 24) and (formula 25) are output to the output lines 508, 511.
ŷ1(n)xe2x80x83xe2x80x83[Formula 24]
ŷ2(n)xe2x80x83xe2x80x83[Formula 25]
The digital signal y1(n) output from the first unknown system 502 and the digital signal (formula 24) output from the first identification device 503 are fed into an adder 507. The digital signal y2 (n) output from the second unknown system 504 and the digital signal (formula 25) output from the second identification device 505 are fed into an adder 510. At this time, it is supposed that the system identification device 503 and system identification device 505 are identified by a representative adaptive algorithm, such as least mean square (LMS) or recursive least square (RLS). The system identification by adaptive algorithm is realized by following calculation. The adder 507 calculates error e1 (n) between the output digital signal y1 (n) of the first unknown system 502 and the output digital signal (formula 24) of the system identification device 503, feeding back the error, and changing the filter coefficient of the first system identification device 503 so that the error e1 (n) may be closer to zero. This is the same in the second system identification device 505. Thus, in the prior art, when identifying the system, the system identification device required the same input digital signal as in the unknown system.
Now suppose to identify an unknown system by employing a method proposed in a paper disclosed in xe2x80x9cA Least-Squares Approach to Blind Channel Identificationxe2x80x9d (IEEE Transactions on Signal Processing Vol. 43, No. 12, 1995, pp. 2982-2993) (hereinafter called reference 1). This is explained by reference to FIG. 1. FIG. 1 is a block diagram showing a general system identification method shown in reference 1. In FIG. 1, first unknown systems 102xcx9cm-th unknown systems 104 show m different unknown systems, or m different unknown systems spuriously decomposing one unknown system. In FIG. 1, m unknown systems are shown, but the generality is not lost if the number of unknown systems is limited to two, and therefore, in the following explanation, the number of unknown systems is limited to two for the sake of simplicity.
In FIG. 1, from one input line 101, a digital signal x (n) is transmitted through two unknown systems 102 and 103, and they output digital signals y1 (n) and y2 (n) to output lines 105 and 106. According to reference 1, in a system identification device 108, using only these two input digital signals y1 (n), y2 (n), it is possible to identify the first unknown system 102 and second unknown system 103. It means that only the outputs of the unknown systems are used as the input to the system identification device 108 to be identified, and the input digital signal x (n) to the unknown systems is not necessary. Besides, xe2x80x9cEqualization Based on Blind System Identification Using Second Order Statistics (a paper included in pre-print A-4-22 of General Assembly of Japan Society of Electronic Information and Communication; hereinafter called reference 2) can be also used for building up a system identification device same as reference 1. The both are known as the blind system.
In reference 1 and reference 2, however, the method of identifying the unknown system by the digital signal output from the unknown system, that is, only the off-line processing is mentioned, and the system identification method in reference 1 or reference 2 cannot be used in on-line processing for updating sequentially while operating the object unknown system, and hence it cannot be used in the adaptive echo canceller requiring real-time processing. Another problem is that the formula development mentioned in reference 1 or reference 2 is not a formula development in which sequential updating process can be introduced.
In the case of an off-line processing, if the characteristic of the unknown system is changed due to some reason (for example, a change in time), a different value from the intended unknown system is identified. To identify according to the method of reference 1 or reference 2, it is necessary to calculate the inverse matrix or eigenvalue, and the quantity of calculation becomes tremendous. In the system identification device and system identification method for use in adaptive echo canceller or the like, and in the recording medium in which the execution program of identification method is recorded, by formula development different from reference 1 or reference 2, that is, by formula development for updating sequentially and not performing operation of inverse matrix, it is required to perform sequential updating process and prevent increase to tremendous quantity of calculation.
It is hence an object of the invention to present a system identification method capable of performing sequential updating process and preventing increase to tremendous quantity of calculation.
To achieve the object, the system identification method of the invention is a system identification method for identifying an unknown system by feeding output digital signal y1 (n) and output digital signal y2 (n) of the unknown system, comprising a first delay step for delaying the input digital signal y2 (n) by one unit time, a formula 2 matrix generating step for generating an input matrix (formula 2) shown in formula 1 from the input digital signal y1 (n) and the digital signal y2 (nxe2x88x921) output at the first delay step, a formula 13 matrix calculating step for calculating the state matrix (formula 13) at the present time shown in formula 12 from the matrix output at the formula 2 matrix generating step and the state matrix (formula 11) calculated one unit time before, a formula 15 matrix calculating step for calculating the state matrix (formula 15) at the present time shown in formula 14 from the matrix output at the formula 2 matrix generating step, the matrix output at the formula 13 matrix calculating step, and the state matrix (formula 11) calculated one unit time before, a second delay step for storing the matrix output at the formula 15 matrix calculating step, a formula 18 matrix calculating step for calculating the state matrix (formula 18) at the present time shown in formula 17 from the matrix output at the formula 2 matrix generating step, input digital signal y2 (n), input digital signal y1 (n), the matrix output at the formula 13 matrix calculating step, and the state matrix (formula 16) calculated one unit time before, a third delay step for storing the matrix output at the formula 18 matrix calculating step, and a formula 19 and formula 20 matrix separating step for separating formula 19 and formula 20 identified at the formula 18 matrix calculating step into a necessary format individually.
As a result, a system identification method capable of performing sequential updating process and preventing increase to tremendous quantity of calculation is obtained.